TY - JOUR
T1 - A new computational approach for modeling diffusion tractography in the brain
AU - Garimella, Harsha T.
AU - Kraft, Reuben H.
N1 - Funding Information:
The authors gratefully acknowledge the support provided by Computational Fluid Dynamics Research Corporation (CFDRC) under a sub-contract funded by the Department of Defense, Department of Health Program through contract W81XWH-14-C-0045. The authors thank Dr. Sam Slobounov and Dr. Brian D. Johnson for the data provided. All the DTI/DSI data used here is being provided by The Pennsylvania State University Center for Sports Concussion Research and Service, University Park, USA. This work was supported in part through an instrumentation grant funded by the National Science Foundation through grant OCI-0821527. We would also like to acknowledge The Pennsylvania State University Social, Life, and Engineering Sciences Imaging Center (SLEIC), High Field MRI Facility for providing access to the imaging equipment. The authors thank The Pennsylvania State University Institute for Cyberscience for providing the computational resources required for this work.
Publisher Copyright:
© 2017, Editorial Board of Neural Regeneration Research. All rights reserved.
PY - 2017/1
Y1 - 2017/1
N2 - Computational models provide additional tools for studying the brain, however, many techniques are currently disconnected from each other. There is a need for new computational approaches that span the range of physics operating in the brain. In this review paper, we offer some new perspectives on how the embedded element method can fill this gap and has the potential to connect a myriad of modeling genre. The embedded element method is a mesh superposition technique used within finite element analysis. This method allows for the incorporation of axonal fiber tracts to be explicitly represented. Here, we explore the use of the approach beyond its original goal of predicting axonal strain in brain injury. We explore the potential application of the embedded element method in areas of electrophysiology, neurodegeneration, neuropharmacology and mechanobiology. We conclude that this method has the potential to provide us with an integrated computational framework that can assist in developing improved diagnostic tools and regeneration technologies.
AB - Computational models provide additional tools for studying the brain, however, many techniques are currently disconnected from each other. There is a need for new computational approaches that span the range of physics operating in the brain. In this review paper, we offer some new perspectives on how the embedded element method can fill this gap and has the potential to connect a myriad of modeling genre. The embedded element method is a mesh superposition technique used within finite element analysis. This method allows for the incorporation of axonal fiber tracts to be explicitly represented. Here, we explore the use of the approach beyond its original goal of predicting axonal strain in brain injury. We explore the potential application of the embedded element method in areas of electrophysiology, neurodegeneration, neuropharmacology and mechanobiology. We conclude that this method has the potential to provide us with an integrated computational framework that can assist in developing improved diagnostic tools and regeneration technologies.
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U2 - 10.4103/1673-5374.198967
DO - 10.4103/1673-5374.198967
M3 - Review article
C2 - 28250733
AN - SCOPUS:85011976017
SN - 1673-5374
VL - 12
SP - 23
EP - 26
JO - Neural Regeneration Research
JF - Neural Regeneration Research
IS - 1
ER -